Systems and methods for scoring driving trips

ABSTRACT

Embodiments of the present invention utilize mobile devices to provide information on a user&#39;s behaviors during transportation. For example, a mobile device carried by a user can be used to detect and analyze driving behaviors during trips in vehicles. The mobile device can further be used to assign and display scores relating to the driving behaviors, scores for individual trips, and an overall driving score for the particular user.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/346,013, entitled “SYSTEMS AND METHODS FOR SCORINGDRIVING TRIPS”, filed Jun. 6, 2016, the contents of which are herebyincorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

Mobile devices, including smart phones, have been utilized to providelocation information to users. Mobile devices can use a number ofdifferent techniques to produce location data. One example is the use ofGlobal Positioning System (GPS) chipsets, which are now widelyavailable, to produce location information for a mobile device. Somesystems have been developed to track driving behaviors including speed,braking, and turn speed. Such systems include external devices that havebeen physically integrated with vehicles to track driving behavior.

SUMMARY OF THE INVENTION

Despite the progress made in relation to collecting data related todrivers and their driving behavior using mobile devices, there is a needin the art for improved methods and systems related to sensor-basedtracking and modifying driving behavior using a mobile device.

Embodiments of the present invention relate to transportation systems.More particularly, embodiments relate to systems and methods for scoringdriving trips based on sensor measurements from a mobile device.

Embodiments of the present invention utilize mobile devices to provideinformation on a user's behaviors during transportation. For example, amobile device carried by a user can be used to detect and analyzedriving behavior. The mobile device can further be used to provide adriving score based on the driving behavior, which may encouragemodification of future driving behavior.

By monitoring a driver's behavior, determining good versus risky drivingbehaviors, and presenting results as part of a graphical userexperience, some embodiments provide data that can be used to influencedriver behavior. As a result, safer driving behavior can be achieved.Some embodiments improve on previous systems by not only collectinginformation on driver behavior, but influencing the driver's behavior toachieve safer driving. Behavior modification provides a broad categoryin which a number of behaviors can be modified using a variety oftechniques and actions.

Thus, some embodiments allow interested parties, such as drivers, toidentify and distinguish good and risky driving behaviors. Someembodiments further help drivers understand and improve their drivingbehavior. As a result, drivers may avoid unnecessary incidents,accidents, and even death. Financially, small impacts improving drivingbehavior (e.g., less distracted driving, less hard braking events, etc.)across a large population of drivers can have a major impact on society,with potential savings on the order of billions of dollars.

According to some embodiments, a method is provided. The methodcomprises obtaining sensor measurements at a measurement rate from oneor more sensors of a mobile device in a vehicle during a trip, andidentifying a user of the mobile device in the vehicle as a driver ofthe vehicle during the trip. The method further comprises monitoring aplurality of behaviors associated with the mobile device during the tripin the vehicle using the sensor measurements. The plurality of behaviorscomprise at least one or all of a braking behavior, an accelerationbehavior, a mobile device usage behavior, or a speeding behavior. In anembodiment in which all of these behaviors are used, the method furthercomprises computing a braking score using the braking behavior,computing an acceleration score using the acceleration behavior,computing a mobile device usage score using the mobile device usagebehavior, and computing a speeding score using the speeding behavior.The method further comprises aggregating the braking score, theacceleration score, the mobile device usage score, and the speedingscore to determine a trip score for the trip in the vehicle. The methodfurther comprises updating the measurement rate for the sensormeasurements from the one or more sensors of the mobile device for asubsequent trip in the vehicle based on the trip score. As describedfurther herein, at least one or all of these behaviors may be used todetermine the trip score.

According to some embodiments, a system is provided. The systemcomprises a mobile device comprising a plurality of sensors, a memory,and a processor coupled to the memory, wherein the processor isconfigured to perform operations including those recited in the methodsdescribed herein.

According to some embodiments, a computer-program product is provided.The computer-program product is tangibly embodied in a non-transitorymachine-readable storage medium of a device. The computer-programproduct includes instructions that, when executed by one or moreprocessors, cause the one or more processors to perform operationsincluding the steps of the methods described herein.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this patent, any or all drawings, and each claim.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the following drawing figures:

FIG. 1 is a system diagram illustrating a driving behavior detection andscoring system including a mobile device according to some embodiments.

FIG. 2 is a system diagram illustrating a driving behavior detection andscoring system including a server according to some embodiments.

FIG. 3 is a flowchart illustrating a driving behavior detection andscoring method according to some embodiments.

FIG. 4 is a flowchart illustrating a method of determining times duringwhich a user is traveling according to some embodiments.

FIG. 5 is a flowchart illustrating a method of determining modes oftransportation during a trip according to some embodiments.

FIG. 6 is a flowchart illustrating a method of determining a location ofa user of a mobile device in a vehicle according to some embodiments.

FIG. 7 is a flowchart illustrating a method of determining mobile deviceusage according to some embodiments.

FIG. 8A illustrates screen shots of a user interface for scoring tripsand determining user driver status according to some embodiments.

FIG. 8B illustrates a screen shot of a user interface for displayingscored and unscored trips according to some embodiments.

FIG. 9 illustrates a bar graph of the distribution of overall drivingscores according to some embodiments.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Certain aspects and embodiments of this disclosure are provided below.Some of these aspects and embodiments may be applied independently andsome of them may be applied in combination as would be apparent to thoseof skill in the art. In the following description, for the purposes ofexplanation, specific details are set forth in order to provide athorough understanding of embodiments of the invention. However, it willbe apparent that various embodiments may be practiced without thesespecific details. The figures and description are not intended to berestrictive.

The ensuing description provides exemplary embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the exemplary embodimentswill provide those skilled in the art with an enabling description forimplementing an exemplary embodiment. It should be understood thatvarious changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the invention as setforth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional steps notincluded in a figure. A process may correspond to a method, a function,a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

The term “computer-readable medium” includes, but is not limited to,portable or non-portable storage devices, optical storage devices, andvarious other mediums capable of storing, containing, or carryinginstruction(s) and/or data. A computer-readable medium may include anon-transitory medium in which data can be stored and that does notinclude carrier waves and/or transitory electronic signals propagatingwirelessly or over wired connections. Examples of a non-transitorymedium may include, but are not limited to, a magnetic disk or tape,optical storage media such as compact disk (CD) or digital versatiledisk (DVD), flash memory, memory or memory devices. A computer-readablemedium may have stored thereon code and/or machine-executableinstructions that may represent a procedure, a function, a subprogram, aprogram, a routine, a subroutine, a module, a software package, a class,or any combination of instructions, data structures, or programstatements. A code segment may be coupled to another code segment or ahardware circuit by passing and/or receiving information, data,arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, or the like.

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, hardware description languages, or anycombination thereof. When implemented in software, firmware, middlewareor microcode, the program code or code segments to perform the necessarytasks (e.g., a computer-program product) may be stored in acomputer-readable or machine-readable medium. A processor(s) may performthe necessary tasks.

FIG. 1 is a system diagram illustrating a system 100 for detecting andscoring driving data according to some embodiments. System 100 includesa mobile device 101 having a number of different components. Mobiledevice 101 may include a sensor data block 105, a data processing block120, a data transmission block 130, and optionally a notification block140. The sensor data block 105 includes data collection sensors as wellas data collected from these sensors that are available to mobile device101. This can include external devices connected via Bluetooth, USBcable, etc. The data processing block 120 includes storage 126, andmanipulations done to the data obtained from the sensor data block 105by processor 122. This may include, but is not limited to, analyzing,characterizing, subsampling, filtering, reformatting, etc. Datatransmission block 130 may include any transmission of the data off themobile device 101 to an external computing device that can also storeand manipulate the data obtained from sensor data block 105. Theexternal computing device can be, for example, a server 150. Server 150can comprise its own processor 152 and storage 156. Notification block140 may report the results of analysis of sensor data performed by thedata processing block 120 to a user of the mobile device 101 via adisplay (not shown). For example, notification block 140 may display orotherwise report a score for a trip or for a plurality of trips to auser of the mobile device 101. In one embodiment, mobile device 101 mayfurther include a scoring block (not shown) to score individual orcollective trips, as described further herein with respect to FIG. 3. Inother embodiments, the scoring may be performed by server 150, asdescribed further herein with respect to FIG. 2.

Some embodiments are described using examples where driving data iscollected using mobile devices 101, and these examples are not limitedto any particular mobile device. As examples, a variety of mobiledevices including sensors such as accelerometers 112, gyroscopes 116,compasses 119, barometers 113, location determination systems such asglobal positioning system (GPS) receivers 110, communicationscapabilities, and the like are included within the scope of theinvention. Exemplary mobile devices include smart watches, fitnessmonitors, Bluetooth headsets, tablets, laptop computers, smart phones,music players, movement analysis devices, and other suitable devices.One of ordinary skill in the art, given the description herein, wouldrecognize many variations, modifications, and alternatives for theimplementation of embodiments.

To collect data associated with the driving behavior of a driver, one ormore sensors on mobile device 101 (e.g., the sensors of sensor datablock 105) are operated close in time to a period when mobile device 101is with the driver when operating a vehicle—also termed herein “a drive”or “a trip”. With many mobile devices 101, the sensors used to collectdata are components of the mobile device 101, and use power resourcesavailable to mobile device 101 components, e.g., mobile device batterypower and/or a data source external to mobile device 101.

Some embodiments use settings of a mobile device to enable differentfunctions described herein. For example, in Apple iOS, and/or AndroidOS, having certain settings enabled can enable certain functions ofembodiments. For some embodiments, having location services enabledallows the collection of location information from the mobile device(e.g., collected by global positioning system (GPS) sensors, andenabling background app refresh allows some embodiments to execute inthe background, collecting and analyzing driving data even when theapplication is not executing. In some implementations, alerts areprovided or surfaced using notification block 140 while the app isrunning in the background since the trip capture can be performed in thebackground. These alerts may facilitate driving behavior modification.Further disclosure regarding scoring and behavior modification may befound in U.S. patent application Ser. No. 15/413,005, filed Jan. 23,2017, herein incorporated by reference in its entirety.

FIG. 2 shows a system 200 for collecting driving data that can include aserver 201 that communicates with mobile device 101. Server 201 may bethe same or a different server than server 150 of FIG. 1. In someembodiments, server 201 may provide functionality using componentsincluding, but not limited to vector analyzer 258, vector determiner259, external information receiver 212, classifier 214, data collectionfrequency engine 252, driver detection engine 254, and scoring engine290. These components may be executed by processors (not shown) inconjunction with memory (not shown). Server 201 may also include datastorage 256. It is important to note that, while not shown, one or moreof the components shown operating within server 201 can operate fully orpartially within mobile device 101, and vice versa.

To collect data associated with the driving behavior of a driver, one ormore sensors on mobile device 101 (e.g., the sensors of sensor datablock 105) may be operated close in time to a period when mobile device101 is with the driver when operating a vehicle—also termed herein “adrive” or “a trip”. Once the mobile device sensors have collected data(and/or in real time), some embodiments analyze the data to determineacceleration vectors for the vehicle, as well as different features ofthe drive. For example, driver detection engine 254 may apply one ormore processes to the data to determine whether the user of the mobiledevice 101 is a driver of the vehicle. Other examples are processes todetect and classify driving features using classifier 214, and determineacceleration vectors using vector analyzer 258 and vector determiner259. In some embodiments, external data (e.g., weather) can be retrievedand correlated with collected driving data.

As discussed herein, some embodiments can transform collected sensordata (e.g., driving data collected using sensor data block 105) intodifferent results, including, but not limited to, analysis of brakingbehavior, analysis of acceleration behavior, estimates of the occurrenceof times where a driver is speeding (“speeding behavior”), and estimatesof the occurrence of times where a driver was distracted (“mobile deviceusage behavior”). Examples of collecting driving data using sensors of amobile device are described herein. Examples of analyzing collecteddriving data to detect driving behaviors are also described herein.Although some embodiments are discussed in terms of certain behaviors,the present invention is not limited to these particular behaviors andother driving behaviors are included within the scope of the presentinvention. The driving behaviors may be used by scoring engine 290 toassign a driving score to a trip or to a plurality of trips based ondriving behaviors. Notifications of driving behavior, such as display ofa driving score, can be made via notification block 140 of mobile device101. The driving score may be used to adjust the frequency of datacollected by sensor data block 105 in some embodiments, as adjusted bydata collection frequency adjustment engine 252. Data collectionfrequency adjustment engine 252 may be in communication with mobiledevice 101 to cause the sensor data block 105 to collect data morefrequently, less frequently, or at the same frequency, as describedfurther herein with respect to FIG. 3.

Although shown and described as being contained within server 201, whichcan be remote from mobile device 101, it is contemplated that any or allof the components of server 201 may instead be implemented within mobiledevice 101, and vice versa. It is further contemplated that any or allof the functionalities described herein may be performed during a drive,in real time, or after a drive.

FIG. 3 is a flowchart 300 illustrating a driving behavior detection andscoring method according to some embodiments. The method includesobtaining sensor measurements at a measurement rate from one or moresensors of a mobile device in a vehicle during a trip (310). The mobiledevice may be, for example, mobile device 101 of FIGS. 1 and/or 2. Thesensor measurements may be driving data, such as that collected by oneor more sensors within sensor data block 105 of FIG. 1. In someembodiments, the measurement rate may initially be set at any value, andmay be adjusted based on the trip score, as described further herein. Insome embodiments, an initial subset of data may be collected or aninitial subset of sensors may be used for a first trip, and a differentor overlapping subset of data may be collected or a different oroverlapping subset of sensors may be used for a second trip, asdescribed further herein.

A trip in a vehicle may be determined by any of a number of methods. Forexample, the start and end times of a trip may be determined accordingto the method described herein with respect to FIG. 4. Sensormeasurements may be taken only between the start and end times of thetrip in some embodiments, in order to prolong battery life of the mobiledevice and to avoid collection of unnecessary or unusable data. A tripin a vehicle (e.g., a car) as opposed to another form of transportationmay be determined according to the method described herein with respectto FIG. 5, for example. Sensor measurements may be taken only when thetrip is determined to be in a vehicle in one embodiment, also in orderto prolong battery life of the mobile device and to avoid collection ofunnecessary or unusable data. In some embodiments, the user of themobile device may explicitly report that s/he is taking a trip, and/orthat s/he is taking a trip in a vehicle.

The method further includes identifying a user of the mobile device as adriver of the vehicle during the trip (315). A variety of methods can beused to make this determination. One exemplary method is discussedfurther herein with respect to FIG. 6. However, it is contemplated thatany of a variety of other methods may be used to determine whether theuser of the device is driving. Alternatively, the user may explicitlyreport that s/he is driving. Sensor measurements may be taken only whenthe user is determined to be the driver in some embodiments, in order toprolong battery life of the mobile device and to avoid collection ofunnecessary or unusable data.

The method further includes monitoring a plurality of behaviorsassociated with the mobile device during the trip in the vehicle usingthe sensor measurements (320). The plurality of behaviors may includeany driving behaviors, such as braking behaviors, accelerationbehaviors, mobile device usage behaviors (also referred to herein as“distracted driving behaviors”), and speeding behaviors. For example,data from an accelerometer indicating rapid deceleration (e.g.,deceleration greater in magnitude than a threshold) may be correlated toa hard braking event indicative of braking behavior. In another example,data from an accelerometer indicating rapid acceleration (e.g.,acceleration greater in magnitude than a threshold) may be correlated toa rapid acceleration event indicative of acceleration behavior. In stillanother example, data from an accelerometer indicating movement,interaction or manipulation of the mobile device by the user within thevehicle during a drive may be used to calculate a mobile device usagepercentage (e.g., the percentage of the trip in the vehicle during whichthe user is interacting with the device) indicative of mobile deviceusage behavior. Determining mobile device usage during a drive isdescribed further herein with respect to FIG. 7. In still anotherexample, data from one or more sensors, in conjunction with availableroad data, may indicate that the user is driving above the speed limit,which may be correlated to speeding behavior.

The driving behaviors may also indicate positive driving behavior, suchas a lack of hard braking events, a lack of speeding events, a lack ofrapid acceleration events, and/or a lack of mobile device usage events.Some embodiments combine data from several sources, for example, driveridentification, vehicle dynamics, and the driver's interaction with themobile device, for instance, touching the screen of the device or movingthe device, while going through a corner or speeding. Cross-correlationbetween the classification of drivers/driving behavior and the driver'sactions while driving provide benefits not available with conventionaltechniques.

The method further includes computing a braking score using the brakingbehavior, computing an acceleration score using the accelerationbehavior, computing a mobile device usage score using the mobile deviceusage behavior, and computing a speeding score using the speedingbehavior (325). The various behavioral scores may be represented by anycombination of letters, numbers, and/or symbols. For exemplary purposes,the scores are discussed with respect to a numerical score between 0 and100 (e.g., “83”) and/or a grade (e.g., “A” for scores between 90-100,“B” for scores between 80-89, “C” for scores between 70-79, “D” forscores between 60-69, and “F” for scores 59 and below).

In some embodiments, a braking score may be computed by comparing thenumber of hard braking events during the trip to an average number (orother statistical value) of hard braking events per trip. The averagenumber of hard braking events per trip may be determined from drivingdata collected for other users during other trips in other vehicles. Inalternative or additional embodiments, the average number of hardbraking events per trip may be determined for the same user for othertrips in the same or other vehicles. For example, if the user had onehard braking event during the trip and the average is one hard brakingevent per trip, a braking score of 75 may be assigned. In anotherexample, if the user had no hard braking events during the trip and theaverage is one hard braking event per trip, a braking score of 100 maybe assigned. In still another example, if the user had 3 hard brakingevents during the trip and the average is one hard braking event pertrip, a braking score of 25 may be assigned. In one embodiment, thebraking score may be curved to or centered on a particular score (e.g.,75 or 80) with respect to other braking scores for other trips of otherusers.

In some embodiments, an acceleration score may be computed by comparingthe number of rapid acceleration events during the trip to an averagenumber (or other statistical value) of rapid acceleration events pertrip. The average number of rapid acceleration events per trip may bedetermined from driving data collected for other users during othertrips in other vehicles. In alternative or additional embodiments, theaverage number of acceleration events per trip may be determined for thesame user for other trips in the same or other vehicles. For example, ifthe user had one rapid acceleration event during the trip and theaverage is one rapid acceleration event per trip, an acceleration scoreof 75 may be assigned. In another example, if the user had no rapidacceleration events during the trip and the average is one rapidacceleration event per trip, an acceleration score of 100 may beassigned. In still another example, if the user had 5 rapid accelerationevents during the trip and the average is one rapid acceleration eventper trip, an acceleration score of 15 may be assigned. In oneembodiment, the acceleration score may be curved to or centered on aparticular score (e.g., 75 or 80) with respect to other accelerationscores for other trips of other users.

In some embodiments, a mobile device usage score may be computed bycomparing the percentage of the trip that the user was interacting withthe mobile device to an average percentage (or other statistical value)of trips in which users interacted with their mobile devices. Theaverage percentage of trips in which users interacted with their mobiledevices may be determined from driving data collected for other usersduring other trips in other vehicles. In alternative or additionalembodiments, the average percentage may be determined for the same userfor other trips in the same or other vehicles. For example, if the userinteracted with the mobile device during 5% of the trip and the averageis 2.5%, a mobile device usage score of 50 may be assigned. In anotherexample, if the user interacted with the mobile device during 2.5% ofthe trip and the average is 2.5%, a mobile device usage score of 75 maybe assigned. In one embodiment, the mobile device usage score may becurved to or centered on a particular score (e.g., 75 or 80) withrespect to other mobile device usage scores for other trips of otherusers.

In some embodiments, a speeding score may be computed by comparing thepercentage of the trip that the user was speeding to an averagepercentage (or other statistical value) of trips in which users werespeeding. The average percentage of trips in which users were speedingmay be determined from driving data collected for other users duringother trips in other vehicles. In alternative or additional embodiments,the average percentage may be determined for the same user for othertrips in the same or other vehicles. For example, if the user wasspeeding 15% of the trip and the average is 20%, a speeding score of 90may be assigned. In this example, although the user sped less than theaverage, a score of 100 may not be assigned because the user stillengaged in risky behavior. In another example, if the user was speeding25% of the trip and the average is 20%, a speeding score of 71 may beassigned. In one embodiment, the speeding score may be curved to orcentered on a particular score (e.g., 75 or 80) with respect to otherspeeding scores for other trips of other users.

The method further includes aggregating the braking score, theacceleration score, the mobile device usage score, and the speedingscore to determine a trip score for the trip in the vehicle (330). Insome embodiments, the braking score, the acceleration score, the mobiledevice usage score, and the speeding score may each be weighted equally(e.g., by multiplying each of the scores by 0.25) and combined todetermine the trip score. In some embodiments, the braking score, theacceleration score, the mobile device usage score, and the speedingscore may be weighted differently based on any criteria, such as theriskiness of the particular behavior. For example, it may be determinedthat the most risky to least risky behaviors are mobile device usagebehaviors, speeding behaviors, braking behaviors, and accelerationbehaviors. Thus, a weight of 0.4 may be assigned to the mobile deviceusage score, a weight of 0.25 may be assigned to the speeding score, aweight of 0.225 may be assigned to the braking score, and a weight of0.125 may be assigned to the acceleration score. Thus, the trip scorewould be (0.4×mobile device usage score)+(0.25×speedingscore)+(0.225×braking score)+(0.125×acceleration score).

Although described herein as determining and aggregating the brakingscore, acceleration score, the mobile device usage score, and thespeeding score, it is contemplated that not all of these scores may bedetermined and/or aggregated in some embodiments. For example, in someembodiments, any combinations of one or more of these scores may beused, such as the braking score and the mobile device usage score, theacceleration score and the speeding score, just the mobile device usagescore, etc.

In some embodiments, a first combination of scores may be determined andaggregated on a first trip to determine a first trip score. A second,different combination of scores may be determined and aggregated on oneor more subsequent trips based on the first trip score. For example, amobile device usage score may only be used on a first trip. If themobile device usage score is low, indcating high mobile device usage,for example, all of the scores may be determined and aggregated on asubsequent trip to take into account other risky driving behaviors thatmay be associated with the mobile device usage.

In some embodiments, the method includes updating the measurement ratefor the sensor measurements from the one or more sensors of the mobiledevice for a subsequent trip in the vehicle based on the trip score(335). For example, a trip score of 100 may indicate that the user is avery good driver. Thus, sensor measurements may be taken less frequentlyin the next trip that the user drives a vehicle because it is unlikelythat risky driving behaviors will be detected. In another example, atrip score of 50 may indicate that the user is a very bad driver. Thus,sensor measurements may be taken more frequently in the next trip thatthe user drives a vehicle because it is likely that more risky drivingbehaviors will be detected.

In some embodiments, the types of sensor measurements made and/or thetypes of sensors used to monitor the driving behaviors may be alteredfor a subsequent trip in the vehicle based on the trip score. Forexample, all available sensors on the mobile device may be used on afirst trip that receives a trip score of 98, indicating that the user isa very good driver. Thus, only one sensor or fewer sensors (e.g.,sensors consuming little battery power) may be used in a subsequent tripin which the user drives a vehicle, because the user is unlikely toengage in risky driving behaviors. Sensors requiring large amounts ofbattery power, such as a GPS, may be deactivated. In another example,only an accelerometer on the mobile device may be used on a first tripthat receives a trip score of 42, indicating that the user is a very baddriver. Thus, additional sensors may be employed in a subsequent trip inwhich the user drives a vehicle, because the user is likely to engage inrisky driving behaviors that should be monitored. Similar methods may beused to determine whether specific types of sensor measurements shouldbe made in subsequent trips (e.g., gravitational accelerationmeasurements).

Once the trip score is calculated, the method may restart at step 310with obtaining sensor measurements from the mobile device in the vehiclefor subsequent trips in which the user is driving. Additional tripscores may be obtained for subsequent trips. In some embodiments, thetrip score for the first trip by the user may be combined with tripscores for subsequent trips by the user to determine an overall scorefor the user. In some embodiments, each of the trip scores may beweighted equally and combined to determine the overall score. In someembodiments, the trip scores may be weighted differently according toany criteria. For example, the trip scores for longer trips (i.e., tripsof greater distance and/or greater duration) may be weighted heavierthan the trip scores for shorter trips (i.e., trips of shorter distanceand/or shorter duration).

In some embodiments, if a distance of any trip used to generate a tripscore used in the overall score is greater than a threshold distance,the driving behaviors may be scaled down to the threshold distance.Scaling driving behaviors for longer trips down to a threshold distanceaccording to this embodiment means that no single trip may have a majorimpact on the overall score. For example, if 4 hard braking events areobserved on a 200 mile trip and the threshold distance is set at 100miles, only 2 hard braking events may be used to determine the brakingscore. In this example, the 4 hard braking events are divided equally toobtain an average number of hard braking events per 100 miles. Inanother example, however, the number of braking events for a specific100 mile section of the 200 mile trip may be determined and used for thebraking score (e.g., the first 100 miles, the middle 100 miles, the last100 miles, etc.). For example, if all 4 hard braking events are observedin the first 100 miles of the 200 mile trip and the threshold distanceis set at the first 100 miles, all 4 hard braking events may be used todetermine the braking score. Other driving behaviors (e.g., accelerationbehaviors, mobile device usage behaviors, speeding behaviors, etc.) maybe similarly scaled down to determine their individual scores.

In some embodiments, the overall score may be limited by a number oftrips and/or an overall distance for all of the trips used in generatingthe overall score. For example, the overall score may only combine tripscores for the last 10 trips. In another example, the overall score maycombine trip scores for the best 10 trips, worst 10 trips, average 10trips, or some combination thereof. In another example, the overallscore may only combine trip scores for the last 1,000 miles driven.These embodiments may ensure that the most relevant and/or most recentdriving behaviors are being considered in the overall score, and thatthe overall score may be “rolling” in that any behavior modificationsmade by the user over time may be reflected in the overall score in atimely manner.

In some embodiments, the overall score may be curved to or centered on aparticular score (e.g., 75 or 80) with respect to other braking scoresfor other trips of other users. FIG. 9 illustrates a bar graph of thedistribution of a random sampling of overall driving scores according toan embodiment. In FIG. 9, the x-axis represents the overall score andthe y-axis represents the number of users with that overall score. Asshown in FIG. 9, the overall scores have been curved to or centered onan overall score of 80, such that the most users (about 125) have anoverall score of 80.

FIG. 4 is a flowchart illustrating a method of determining times duringwhich a user is traveling according to an embodiment of the invention.The method includes determining one or more classifications for the userat step 410. These classifications include walking, driving, stationary,and other, and may be determined by using an accelerometer in the user'sdevice and classifying the rate of movement of the user's device. Themethod further includes detecting an entry signal which corresponds tothe last walking classification before the driving state is found atstep 412. The method further includes determining a last drivingclassification for the user before a walking classification is foundagain at step 414. The method further include detecting an exit signalcorresponding to the first walking classification after the drivingevent at step 416. The method further includes determining the precisetime stamps of the beginning and end of a drive at step 418. Data duringthis window can be collected and analyzed in real-time, or after thedrive. This method is described further in U.S. patent application Ser.No. 14/139,510, filed Dec. 23, 2013, entitled “METHODS AND SYSTEMS FORDRIVER IDENTIFICATION”, herein incorporated by reference in itsentirety.

FIG. 5 is a flowchart illustrating a method of determining modes oftransportation during a trip according to an embodiment of theinvention. This method may be used, for example, to determine that auser of a device is traveling by car, and not by another method. Themethod illustrated in FIG. 5 may utilize contextual data to removenon-driving modes of transportation from segments of the trip, resultingin the determination of driving segments during the trip. If the tripconsists of solely non-driving modes, then the trip can be identified assuch and not used to analyze the user's driving behaviors.

In the embodiments illustrated in FIG. 5, contextual data, also referredto as contextual map data, is utilized in determining the modes oftransportation during a trip. The contextual map data can be stored in adatabase that includes data related to transportation systems, includingroads, trains, buses, bodies of water, and the like. As an example,location data related to trains could include locations of trainstations, locations of train tracks, timetables and schedules, and thelike. Furthermore, location data related to a bus system could includebus routes, bus schedules, bus stops, and the like.

Referring to FIG. 5, the method includes receiving trip data at step 501and receiving contextual data at step 505. Typically, the trip data ismeasured using a mobile device, such as a smart phone. The trip data caninclude location data (e.g., GPS data) as a function of time,accelerometer data, combinations thereof, or the like. In someembodiments, in order to prolong battery life, only location/GPS data isutilized, whereas in other embodiments, the location data issupplemented with accelerometer data. One of ordinary skill in the artwould recognize many variations, modifications, and alternatives.

In some embodiments, the trip data may be analyzed to determine whenstops are present in the trip data. As an example, using a mobiledevice, the velocity of the mobile device can be determined by analyzingthe location data as a function of time. When the velocity of the mobiledevice drops below a threshold or is equal to zero for a predeterminedperiod of time, a stop in the trip data can be determined. Thus, a tripcan be broken down into segments based on the speed of the user.Wherever the measured speed is close to zero, the corresponding GPSpoint marks the beginning or end of a segment. Once the segments havebeen created, algorithms can be used as described below to determine themode of transportation for the segment. In some embodiments, thesegments are then grouped based on the determined mode to form stages.

Accordingly, the method further includes forming segments by definingthe segments as time periods between stops in the trip data at step 510.Accordingly, for a given trip, a number of segments can be formed, witheach segment separated by a stop in the trip data. As an example, if aperson using the mobile device is riding on a bus, every time the busstops can be defined as a segment. The contextual data can be used todetermine that one or more of the segments are associated with a bus andthe segments can be marked as bus segments. As contiguous segments areassociated with a bus, a stage can be formed by linking togethercontiguous segments to form a stage of the trip associated with travelon a bus. Other modes of transportation can be defined based on segmentsand stages as well. In some embodiments, segments and stages can beassociated with differing modes of transportation, such as walkingbefore and after a bus stage.

The method also includes determining if segments of the trip areassociated with planes at step 512 and removing these segments of thetrip that are associated with airplanes. Segments are analyzed so thatsegments not associated with car travel (for example, starting withplane segments) are removed from the data set, leaving a data set onlyincluding car segments. Accordingly, driving data can be separated fromother modes of transportation and driving behavior can be analyzed.

Returning to the classification performed at decision block 512, thecontextual data received at step 505 includes locations of airports,airport runways, and the like. The location of the points in the tripare compared to the locations associated with airports, which can berepresented by airport polygons. Although a taxiing plane can becharacterized by speeds comparable to vehicle traffic, the location ofthe taxiing plane on a runway enables these points in the trip to beremoved from the data set as a non-driving event. Thus, both locationand vehicle speed as determined using the mobile device can be used todetermine that a segment of a trip is associated with a plane. In someembodiments, the segment/stage of the trip associated with an airplaneare marked accordingly.

The method further includes marking the segments of the trip that areassociated with planes at step 514. If the trip data does not includeany segments associated with a plane, then the method proceeds todeterminations related to other modes of public transportation.

The method also includes determining if segments of the trip areassociated with trains at step 516 and removing segments of the tripassociated with trains. According to embodiments of the presentinvention, the category of trains can include various rail-basedtransportation systems, including commuter trains, light rail, subways,elevated-track trains, and the like. Accordingly, the use of the term“train” should be understood to include these rail-based transportationsystems.

Data about the trip is used in conjunction with contextual data todetermine segments of the trip that are associated with train travel andsegments that are not associated with train travel. Although a train canmove at speeds comparable to vehicle traffic, the location of the traintracks enables these points in the trip to be removed from the data setas a non-driving event. In the embodiment illustrated in FIG. 5, themethod further includes marking the segments/stages of the tripassociated with a train accordingly at step 518. If the trip data doesnot include any segments associated with a train, then the methodproceeds to determinations related to other modes of publictransportation.

The method further includes determining if segments of the trip areassociated with a bus at decision block 520, and if so, removing thesesegments of the trip at step 522. Segments associated with a bus can beidentified, for example, by using contextual data such as bus routes,and correlating the travel path or stopping pattern to the bus route.The method further includes determining if segments of the trip areassociated with any other modes of public transportation such as, forexample, a ferry at step 524. If so, the method further includesremoving these segments at step 526. The method further includes markingthe remaining segments as car segments at step 528. Although describedwith respect to a particular method, it is contemplated that othermethods may be used to determine modes of transportation. This methodand other methods are described in U.S. patent application Ser. No.15/149,628, filed May 9, 2016, entitled “MOTION DETECTION SYSTEM FORTRANSPORTATION MODE ANALYSIS”, herein incorporated by reference in itsentirety.

FIG. 6 is a flowchart illustrating a method of determining a location ofa user of a mobile device in a according to an embodiment of theinvention, in order to determine whether the user is driving thevehicle. The method illustrated in FIG. 6 provides a direct method ofdetermining where a user and/or a user's mobile device is located in avehicle and if the user is driving the vehicle during a driving event.The method includes extracting data from the window around the entrysignal time stamp at step 610. It should be noted that the methodsdescribed in relation to FIG. 6 utilize information related to the entryof the user into the vehicle as described herein. When an individualenters and exits a vehicle, their body turns at least some minimum anglethreshold (e.g., 40 degrees) about the z-axis (the z axis, is alignedwith gravity, and the yaw is defined as the angular distance in acounter-clockwise direction around the z-axis). After merging thewindows form the last walking and first driving classification, we canlook in this merged window for the exact time when a user turns aboutthe z axis at least some minimum threshold angle. This allowsembodiments of the invention to narrow down the window to the exactpoint of entry.

In order to determine if the user entered the vehicle from the left orright, several algorithms can be used in conjunction with each other orseparately. As an example, after identifying a precise time window ofwhen the user has entered the vehicle, the user is identified as beingon the left or the right side of the vehicle. This is determined usingone, multiple, or all of the methods described below.

As illustrated in FIG. 6, the method further includes running a left vs.right enter yaw algorithm to determine if the driver entered from theleft or the right side of the vehicle at step 612. As driving begins,the method further includes detecting an initial (e.g., the first)acceleration event at step 614. Although a first acceleration event isdetected in some embodiments, other implementations will utilize anacceleration event early in the driving event, although not the firstacceleration event.

Given the detection of the initial acceleration event, the method alsoincludes aligning the phone to the vehicle's (e.g., a car's) referenceframe at step 616. Given the alignment of the phone to the referenceframe of the vehicle, the method further includes utilizing anacceleration-based algorithm to determine if the driver entered on theleft or right side of the vehicle at step 618.

The method further includes determining if the user is in the front orback of the vehicle at step 620. One of multiple methods may be utilizedto determine if the user is in the front or back of the vehicle.

Referring once again to FIG. 6, the method includes determining theuser's exit from the vehicle. This includes detecting a terminalacceleration (i.e., a braking event) near or at the end of the drivingevent at step 622. In some implementations, the last braking event inthe driving event is determined. As the driving event ends, a window canbe created around the user's exit from the vehicle, with the windowbased on or including the end of the driving classification and thefirst walking classification after the driving event. The last brakingevent will be in this window.

After driving of the vehicle, the alignment between the phone and thevehicle's reference frame can change. Accordingly, the method furtherincludes aligning the phone to the vehicle's reference frame after thelast braking event is detected at step 624 and extracting data from thewindow around the exit signal timestamp at step 626.

In order to determine which side of the vehicle a user exited or waslocated during the driving event, the method further includes utilizingone or more left vs. right exit algorithms at step 630, including ayaw-based algorithm. In some embodiments, a weighted average of the leftvs. right algorithms is computed. The method further includesdetermining the left vs. right and front vs. back location of theuser/phone in the vehicle at step 632.

Further disclosure regarding driver identification methods that may beused in conjunction with some embodiments may be found in U.S. patentapplication Ser. No. 14/139,510, filed Dec. 23, 2013, entitled “METHODSAND SYSTEMS FOR DRIVER IDENTIFICATION”; and U.S. patent application Ser.No. 15/479,991, filed Apr. 5, 2017, entitled “SYSTEMS AND METHODS FORINDIVIDUALIZED DRIVER PREDICTION”, both of which are herein incorporatedby reference in their entireties.

It should be appreciated that the specific steps illustrated in FIG. 6provide a particular method of determining a location of a driver in avehicle according to an embodiment of the present invention. Othersequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 6 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 7 is a flowchart illustrating a method of determining mobile deviceusage associated with a driver according to an embodiment of theinvention. This determination can be accomplished by collecting andstoring sensor data obtained from the mobile device at step 710, thenanalyzing the data at a server. In some embodiments this analysis can beperformed using resources in the mobile device instead.

In different examples, this measured movement can be minor (e.g., amobile device sliding sideways within a cup holder), or more substantial(e.g., the mobile device being picked up out of the cup holder and heldto the ear of a driver). The method includes analyzing the movementmeasurements to determine whether they are indicative of a particulartype of event occurring with respect to the mobile device in a vehicleat decision block 720. In some embodiments, this particular type ofevent is associated with use by the driver of mobile device, such thatthe driver of the vehicle is potentially not paying attention to drivingtasks (e.g., the driver is distracted from driving tasks by the mobiledevice). For convenience, as used herein, inattentiveness, distraction,failing to pay attention, mobile device usage, and/or other similarterms and phrases broadly signify a driver not paying proper attentionto tasks associated with safely operating the vehicle.

It is important to note that, for any analysis described herein,exclusions, thresholds and other limitations can be applied to allow fortuning of results. For example, certain types of applications (e.g.,navigation applications, hands free phone calls) can be excluded in someembodiments from being assessed as distracting. In addition, a thresholdcan be applied to different analysis (e.g., a certain amount ofmovements must be detected to conclude a mobile device is being used).

As discussed below, not all movement measurements are associated withthe driver of the vehicle, and not all uses of a mobile device by thedriver lead to inattentiveness. It should be appreciated that differentembodiments described herein provide the framework for measuringinattentiveness, but the thresholds and classifications used for thisanalysis is tunable. The definitions of distraction used by embodimentscan be changed at any time, if needed. Some entities may want toflexibly define the potential for distraction of a given activity basedon the demographics of the driver (e.g., hands-free use of a mobiledevice by a 21 year old person is distracting to some degree, while useby a 45 year old person is not). This incorporation of a wide variety ofrelevant factors in determining distraction is described further herein,and different factors can be applied at different phases in the analysis(e.g., picking up a mobile device can be determined to be indicative ofdriver inattentiveness at decision block 720, but later analysisdetermines that the mobile device is being used by a passenger and isnot distracting to the driver).

Examples of which types of movement measurements may be found, by someembodiments, indicative of distraction by decision block 720, and howthese movement measurements may be collected and analyzed, is discussedbelow.

The method further includes estimating the activity being performed withthe mobile device by some embodiments at step 730. Examples of estimatedactivities include, but are not limited to: (1) the mobile device isestimated to be being held to the driver's (or a passenger's) ear, (2)the mobile device is estimated to be being held by the driver (orpassenger) such that the screen can be viewed, (3) the mobile device isbeing held for viewing and the driver (or passenger) is interacting withthe device (e.g., purposefully touching the touch screen and/or hardwarekeyboard of the mobile device), (4) the mobile device is being movedfrom one location in the vehicle (e.g., a pocket, the cup holder) toanother location (e.g., a bracket mount closer to the driver's field ofview). Discussion of the types of processes used by some embodiments todifferentiate between the driver of a vehicle and passengers arediscussed herein.

In some embodiments, the estimate of the activity being performed withthe mobile device at step 730 is accomplished by the interpretation ofmovement measurements alone (e.g., an embodiment is not receiving statusinformation from the mobile device as to what applications (texting,email, phone) are being executed on the device). As would be appreciatedby one having skill in the relevant art(s), given the disclosure herein,the mobile devices vary in their ability to report this statusinformation to some embodiments. For example, an application (e.g., someembodiments) executing on some iPhones, using some versions of AppleIOS, may not be able to receive the status of other executingapplications, for any reason. With other mobile devices (e.g., thoseoperating with Android OS), an application (e.g., some embodiments) candetermine which other applications are executing.

The method further includes, in some embodiments, based on the timeframe of the potentially distracting activity, identifying aggravatingand/or mitigating factors at optional step 750. Though the attachedfigures illustrating embodiments should be interpreted such that, insome embodiments, all steps/components could be optional, combined withother steps/components and/or performed by different steps/components,step 750 has a dotted line to further emphasize the optional nature ofthis stage. This stage should not be interpreted to be more or lessoptional that other figure elements that do not have dotted lines.

Generally speaking, aggravating and mitigating factors are identified bycross-referencing one or more of pieces of external information thatinclude, but are not limited to: the time period of the identifiedactivity (e.g., from 8:12 PM to 8:22 PM), the location of the identifiedactivity from GPS measurements (e.g., Maple Road), the demographics ofthe person identified to be the driver of the vehicle (e.g., 22 year oldmale), the reported weather conditions at the location (e.g., snowing),any special status of the location (e.g., a school zone), the speed ofthe vehicle (e.g., 15 miles per hour), time of day (e.g. at dusk),calendar date (e.g., Christmas Eve), and/or any other similar types ofinformation relevant to how distracting an activity could be. Examplesof potential mitigating factors include, but are not limited to: theslow speed of the vehicle at the time of the event, the experience ofthe driver, and the lack of traffic on the road. One having skill in therelevant art(s), given the description herein, will appreciate the broadvariety of data sources that can be combined to provide additionalaggravating and mitigating factor relevant to distracted driving.

Based on analysis of the kind described above, the method furtherincludes estimating the level of driver inattentiveness (e.g., mobiledevice usage behaviors) during a particular time period at step 760.Once an estimate is completed by some embodiments, a mobile device usagescore can be generated, as described further herein. One having skill inthe relevant art(s), given the description herein, will appreciate thatletter grades, for example, provide an easy to understand rating fordifferent levels of distraction (e.g., an “F” assigned to a singleevent, and/or a drive that contained the event, where an incident wasdetected where a driver sent a text message while the vehicle wasmoving). As described herein, grades assigned to smaller behaviorfeatures (e.g., a single event), can be weighted and/or aggregated intoan evaluation of larger behavior features (e.g., a whole drive, a monthof driving, the driver's current behavior rating, and/or other similarfeatures).

Further disclosure regarding systems and methods for detectingdistracted driving or mobile device usage that may be used inconjunction with embodiments of the invention may be found in U.S.patent application Ser. No. 15/268,049, filed Sep. 16, 2016, entitled“METHODS AND SYSTEMS FOR DETECTING AND ASSESSING DISTRACTED DRIVERS”,which is herein incorporated by reference in its entirety.

FIG. 8A illustrates screen shots of a user interface for scoring tripsand determining user driver status according to an embodiment of theinvention. In this example, scores associated with five trips aredisplayed. As an example, the first trip was at 6:02 PM from Allston toBrookline and was scored as a “B” score. As shown in FIG. 8A, the usercan select or confirm on the summary screen 810 whether or not s/he wasthe driver during particular drives in one embodiment. For example, forthe Cambridge to Allston trip, the user has confirmed that s/he was thedriver. For this trip, the score does not appear, as it has been pushedoff the screen for the user to confirm that s/he was the driver. Inanother example, for the Allston to Coolidge trip, the user hasindicated that s/he was not the driver. Thus, this trip will be removedfrom the summary screen 810, and the trip's individual score will not beused to calculate the overall driver score of 84 in this example. Inorder to prevent users from removing low scoring trips for which theywere the driver, some embodiments may not display particular tripscores, and instead only ask the user to confirm whether s/he was thedriver. In other embodiments, some embodiments ask the driver to confirmdriver or passenger status before a particular trip score is displayed.

Scored trips screen 820 summarizes all scored trips. Additional detailsrelated to each scored trip can be obtained by selecting a trip, whichmay result in the display of a trip-specific screen showing data aboutthe trip, including the number of hard brakes (and/or the brakingscore), the number of rapid accelerations (and/or the accelerationscore), percentage of the trip in which the user interacted with themobile device (and/or the mobile device usage score), percentage of thetrip in which the user was speeding (and/or the speeding score), and thelike.

FIG. 8B illustrates a screen shot of all trips, both scored andunscored, depending on whether the trip was in a vehicle (and notanother mode of transportation), and whether the user was the driver fora trip. For example, FIG. 8B illustrates a flight from Boston to NewYork, a drive from Cambridge to Allston, a drive from Brookline toAllston, and a train ride from Boston to Providence. The drives fromCambridge to Allston and from Brookline to Allston have been scored,while the flight and train ride have not been scored. The differentmodes of transportation taken by the user may be determined as discussedfurther herein with respect to FIG. 5.

As noted, the computer-readable medium may include transient media, suchas a wireless broadcast or wired network transmission, or storage media(that is, non-transitory storage media), such as a hard disk, flashdrive, compact disc, digital video disc, Blu-ray disc, or othercomputer-readable media. The computer-readable medium may be understoodto include one or more computer-readable media of various forms, invarious examples.

In the foregoing description, aspects of the application are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the invention is not limited thereto. Thus,while illustrative embodiments of the application have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. Various features and aspects of theabove-described invention may be used individually or jointly. Further,embodiments can be utilized in any number of environments andapplications beyond those described herein without departing from thebroader spirit and scope of the specification. The specification anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive. For the purposes of illustration, methods were described ina particular order. It should be appreciated that in alternateembodiments, the methods may be performed in a different order than thatdescribed.

Where components are described as performing or being “configured to”perform certain operations, such configuration can be accomplished, forexample, by designing electronic circuits or other hardware to performthe operation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software,firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purposes computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas random access memory (RAM) such as synchronous dynamic random accessmemory (SDRAM), read-only memory (ROM), non-volatile random accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates program code in the form of instructions or datastructures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured for encodingand decoding, or incorporated in a combined video encoder-decoder(CODEC).

The examples and embodiments described herein are for illustrativepurposes only. Various modifications or changes in light thereof will beapparent to persons skilled in the art. These are to be included withinthe spirit and purview of this application, and the scope of theappended claims, which follow.

What is claimed is:
 1. A method comprising: obtaining sensormeasurements at a measurement rate from one or more sensors of a mobiledevice in a vehicle during a trip; identifying a user of the mobiledevice in the vehicle as a driver of the vehicle during the trip;monitoring, by the mobile device, behaviors associated with the mobiledevice during the trip in the vehicle using the sensor measurements, thebehaviors comprising braking behaviors, acceleration behaviors, mobiledevice usage behaviors, and speeding behaviors; computing, by the mobiledevice, a braking score using the braking behaviors, an accelerationscore using the acceleration behaviors, a mobile device usage scoreusing the mobile device usage behaviors, and a speeding score using thespeeding behaviors; aggregating, by the mobile device, the brakingscore, the acceleration score, the mobile device usage score, and thespeeding score to determine a trip score for the trip in the vehicle;and updating the obtaining of the sensor measurements for a subsequenttrip in the vehicle based on the trip score, wherein updating theobtaining of the sensor measurements comprises: adjusting themeasurement rate for the sensor measurements for the subsequent trip, orselecting a different one or more sensors of the mobile device for thesensor measurements for the subsequent trip.
 2. The method of claim 1,further comprising: combining the trip score for the trip in the vehiclewith other trip scores associated with other trips in which the user ofthe mobile device is the driver of the vehicle to determine an overallscore for the user of the mobile device.
 3. The method of claim 2,wherein if a trip distance associated with the trip in the vehicle isabove a threshold distance, scaling the braking behaviors, theacceleration behaviors, the mobile device usage behaviors, and thespeeding behaviors down to the threshold distance.
 4. The method ofclaim 2, wherein an overall distance associated with the trip and theother trips is at or below a threshold overall distance.
 5. The methodof claim 1, wherein aggregating the braking score, the accelerationscore, the mobile device usage score, and the speeding score comprises:weighting the braking score, the acceleration score, the mobile deviceusage score, and the speeding score; and combining the weighted brakingscore, the weighted acceleration score, the weighted mobile device usagescore, and the weighted speeding score.
 6. The method of claim 1,further comprising: comparing the trip score for the trip in the vehiclewith other trip scores associated with other trips in which other usersof other mobile devices are driving other vehicles; and curving the triscore and the other trip scores to be centered at a selected score. 7.The method of claim 1, wherein computing the braking score using thebraking behaviors comprises: comparing the braking behaviors during thetrip in the vehicle to other braking behaviors during other trips byother users in other vehicles.
 8. The method of claim 7, whereincomparing the braking behaviors to other braking behaviors comprises:counting a number of hard braking events during the trip in the vehicle,wherein hard braking events are indicated by a deceleration measured bythe one or more sensors of the mobile device being greater in magnitudethan a threshold deceleration; and comparing the number of hard brakingevents during the trip in the vehicle to other numbers of hard brakingevents during other trips by other users in other vehicles.
 9. Themethod of claim 1, wherein computing the acceleration score using theacceleration behaviors comprises: comparing the acceleration behaviorsduring the trip in the vehicle to other acceleration behaviors duringother trips by other users in other vehicles.
 10. The method of claim 9,wherein comparing the acceleration behaviors to other accelerationbehaviors comprises: counting a number of rapid acceleration eventsduring the trip in the vehicle, wherein rapid acceleration events areindicated by an acceleration of the mobile device measured by the one ormore sensors of the mobile device being greater in magnitude than athreshold acceleration; and comparing the number of rapid accelerationevents during the trip in the vehicle to other numbers of rapidacceleration events during other trips by other users in other vehicles.11. The method of claim 1, wherein computing the mobile device usagescore using the mobile device usage behaviors comprises: comparing themobile device usage behaviors during the trip in the vehicle to othermobile device usage behaviors during other trips by other users in othervehicles.
 12. The method of claim 11, wherein comparing the mobiledevice usage behaviors to other mobile device usage comprises:determining a mobile device usage percentage indicated by a percentageof the trip in the vehicle during which the user is interacting with themobile device; and comparing the mobile device usage percentage to othermobile device usage percentages for other trips by other users in othervehicles.
 13. The method of claim 1, wherein computing the speedingscore using the speeding behaviors comprises: comparing the speedingbehaviors during the trip in the vehicle to other speeding behaviorsduring other trips by other users in other vehicles.
 14. The method ofclaim 13, wherein comparing the speeding behaviors to other speedingbehaviors comprises: determining a speeding percentage indicated by apercentage of the trip in the vehicle during which the vehicle isspeeding; and comparing the speeding percentage to other speedingpercentages for other trips by other users in other vehicles.
 15. Amobile device comprising: a plurality of sensors; a memory; and aprocessor coupled to the memory, wherein the processor is configured toperform operations including: obtaining sensor measurements at ameasurement rate from one or more sensors of the plurality of sensors ofthe mobile device in a vehicle during a trip; identifying a user of themobile device in the vehicle as a driver of the vehicle during the trip;monitoring, by the mobile device, behaviors associated with the mobiledevice during the trip in the vehicle using the sensor measurements, thebehaviors comprising braking behaviors, acceleration behaviors, mobiledevice usage behaviors, and speeding behaviors; computing, by the mobiledevice, a braking score using the braking behaviors, an accelerationscore using the acceleration behaviors, a mobile device usage scoreusing the mobile device usage behaviors, and a speeding score using thespeeding behaviors; aggregating, by the mobile device, the brakingscore, the acceleration score, the mobile device usage score, and thespeeding score to determine a trip score for the trip in the vehicle;and updating the obtaining of the sensor measurements for a subsequenttrip in the vehicle based on the trip score, wherein updating theobtaining of the sensor measurements comprises: adjusting themeasurement rate for the sensor measurements for the subsequent trip, orselecting a different one or more sensors of the plurality of sensors ofthe mobile device for the sensor measurements for the subsequent trip.16. The mobile device of claim 15, wherein the operations furtherinclude: combining the trip score for the trip in the vehicle with othertrip scores associated with other trips in which the user of the mobiledevice is the driver of the vehicle to determine an overall score forthe user of the mobile device.
 17. The mobile device of claim 16,wherein if a trip distance associated with the trip in the vehicle isabove a threshold distance, scaling the braking behaviors, theacceleration behaviors, the mobile device usage behaviors, and thespeeding behaviors down to the threshold distance.
 18. The mobile deviceof claim 16, wherein an overall distance associated with the trip andthe other trips is at or below a threshold overall distance.
 19. Themobile device of claim 15, wherein aggregating the braking score, theacceleration score, the mobile device usage score, and the speedingscore comprises: weighting the braking score, the acceleration score,the mobile device usage score, and the speeding score; and combining theweighted braking score, the weighted acceleration score, the weightedmobile device usage score, and the weighted speeding score.
 20. Themobile device of claim 15, wherein the operations further include:comparing the trip score for the trip in the vehicle with other tripscores associated with other trips in which other users of other mobiledevices are driving other vehicles; and curving the trip score and theother trip scores to be centered at a selected score.
 21. The method ofclaim 1, wherein adjusting the measurement rate comprises: increasingthe measurement rate based on the trip score being below a firstthreshold; or decreasing the measurement rate based on the trip scorebeing above a second threshold.
 22. The method of claim 1, wherein themobile device usage behaviors are monitored based on monitoring arelative movement of the mobile device with respect to the vehicle. 23.The method of claim 1, wherein each of the braking score, theacceleration score, the mobile device usage score, and the speedingscore are scaled based on a distance of the trip; and wherein the tripscore is computed based on aggregating the scaled braking score, thescaled acceleration score, the scaled mobile device usage score, and thescaled speeding score.